TY - GEN
T1 - An iterated conditional modes solution for sparse Bayesian factor modeling of transcriptional regulatory networks
AU - Meng, Jia
AU - Zhang, Jianqiu
AU - Chen, Yidong
AU - Huang, Yufei
PY - 2010
Y1 - 2010
N2 - The problem of uncovering transcriptional regulation by transcription factors (TFs) based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM) coupled with its ICM solution is proposed. BSCRFM models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF regulated genes and it admits prior knowledge from existing database regarding TF regulated target genes. An efficient Iterated Conditional Modes (ICM) algorithm is developed, and a maximum a posterior (MAP) solution is calculated from multiple ICM results to avoid the local maximum problem, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can then be obtained. The proposed model's ICM algorithm and MAP solution are evaluated on the simulated systems and results demonstrated the validity and effectiveness of the proposed approach. The proposed model is also applied to the breast cancer microarray data and a TF regulated network is obtained.
AB - The problem of uncovering transcriptional regulation by transcription factors (TFs) based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM) coupled with its ICM solution is proposed. BSCRFM models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF regulated genes and it admits prior knowledge from existing database regarding TF regulated target genes. An efficient Iterated Conditional Modes (ICM) algorithm is developed, and a maximum a posterior (MAP) solution is calculated from multiple ICM results to avoid the local maximum problem, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can then be obtained. The proposed model's ICM algorithm and MAP solution are evaluated on the simulated systems and results demonstrated the validity and effectiveness of the proposed approach. The proposed model is also applied to the breast cancer microarray data and a TF regulated network is obtained.
UR - http://www.scopus.com/inward/record.url?scp=79952387582&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2010.5706587
DO - 10.1109/BIBM.2010.5706587
M3 - Conference Proceeding
AN - SCOPUS:79952387582
SN - 9781424483075
T3 - Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
SP - 335
EP - 340
BT - Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
T2 - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
Y2 - 18 December 2010 through 21 December 2010
ER -
